Overview

Dataset statistics

Number of variables12
Number of observations124494
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory11.4 MiB
Average record size in memory96.0 B

Variable types

DateTime1
Text1
Categorical1
Numeric9

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
metric7 is highly overall correlated with metric8High correlation
metric8 is highly overall correlated with metric7High correlation
failure is highly imbalanced (99.0%)Imbalance
metric2 is highly skewed (γ1 = 23.8579234)Skewed
metric3 is highly skewed (γ1 = 82.712278)Skewed
metric4 is highly skewed (γ1 = 41.50261118)Skewed
metric7 is highly skewed (γ1 = 73.47645637)Skewed
metric8 is highly skewed (γ1 = 73.47645637)Skewed
metric9 is highly skewed (γ1 = 49.89927809)Skewed
metric2 has 118110 (94.9%) zerosZeros
metric3 has 115359 (92.7%) zerosZeros
metric4 has 115156 (92.5%) zerosZeros
metric7 has 123036 (98.8%) zerosZeros
metric8 has 123036 (98.8%) zerosZeros
metric9 has 97358 (78.2%) zerosZeros

Reproduction

Analysis started2024-03-19 05:09:08.920756
Analysis finished2024-03-19 05:09:28.011582
Duration19.09 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

date
Date

Distinct304
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
Minimum2015-01-01 00:00:00
Maximum2015-11-02 00:00:00
2024-03-19T10:39:28.145003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:28.390739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device
Text

Distinct1169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:29.071742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000008
Min length8

Characters and Unicode

Total characters995953
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS1F01085
2nd rowS1F0166B
3rd rowS1F01E6Y
4th rowS1F01JE0
5th rowS1F01R2B
ValueCountFrequency (%)
z1f0qlc1 304
 
0.2%
w1f0fzpa 304
 
0.2%
s1f0kycr 304
 
0.2%
z1f0qk05 304
 
0.2%
z1f0q8rt 304
 
0.2%
z1f0ql3n 304
 
0.2%
w1f0sjj2 304
 
0.2%
z1f0kjds 304
 
0.2%
z1f0ge1m 304
 
0.2%
z1f0gb8a 304
 
0.2%
Other values (1159) 121454
97.6%
2024-03-19T10:39:29.838406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 191997
19.3%
F 138461
13.9%
0 91213
 
9.2%
S 76253
 
7.7%
W 54973
 
5.5%
Z 39881
 
4.0%
L 25058
 
2.5%
3 23981
 
2.4%
K 18757
 
1.9%
B 17806
 
1.8%
Other values (25) 317573
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 593678
59.6%
Decimal Number 402274
40.4%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 138461
23.3%
S 76253
12.8%
W 54973
 
9.3%
Z 39881
 
6.7%
L 25058
 
4.2%
K 18757
 
3.2%
B 17806
 
3.0%
R 17716
 
3.0%
J 17511
 
2.9%
G 17271
 
2.9%
Other values (14) 169991
28.6%
Decimal Number
ValueCountFrequency (%)
1 191997
47.7%
0 91213
22.7%
3 23981
 
6.0%
6 15880
 
3.9%
5 15303
 
3.8%
2 14013
 
3.5%
4 13679
 
3.4%
7 12224
 
3.0%
9 12042
 
3.0%
8 11942
 
3.0%
Other Punctuation
ValueCountFrequency (%)
¿ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 593678
59.6%
Common 402275
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 138461
23.3%
S 76253
12.8%
W 54973
 
9.3%
Z 39881
 
6.7%
L 25058
 
4.2%
K 18757
 
3.2%
B 17806
 
3.0%
R 17716
 
3.0%
J 17511
 
2.9%
G 17271
 
2.9%
Other values (14) 169991
28.6%
Common
ValueCountFrequency (%)
1 191997
47.7%
0 91213
22.7%
3 23981
 
6.0%
6 15880
 
3.9%
5 15303
 
3.8%
2 14013
 
3.5%
4 13679
 
3.4%
7 12224
 
3.0%
9 12042
 
3.0%
8 11942
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995951
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 191997
19.3%
F 138461
13.9%
0 91213
 
9.2%
S 76253
 
7.7%
W 54973
 
5.5%
Z 39881
 
4.0%
L 25058
 
2.5%
3 23981
 
2.4%
K 18757
 
1.9%
B 17806
 
1.8%
Other values (23) 317571
31.9%
None
ValueCountFrequency (%)
à 1
50.0%
¿ 1
50.0%

failure
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
0
124388 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124494
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

Length

2024-03-19T10:39:30.026543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-19T10:39:30.203062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124494
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124494
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124388
99.9%
1 106
 
0.1%

metric1
Real number (ℝ)

Distinct123877
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.223881 × 108
Minimum0
Maximum2.4414048 × 108
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:30.327951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12090104
Q161284762
median1.2279739 × 108
Q31.8330964 × 108
95-th percentile2.3187385 × 108
Maximum2.4414048 × 108
Range2.4414048 × 108
Interquartile range (IQR)1.2202488 × 108

Descriptive statistics

Standard deviation70459334
Coefficient of variation (CV)0.57570411
Kurtosis-1.1993057
Mean1.223881 × 108
Median Absolute Deviation (MAD)61032236
Skewness-0.011142964
Sum1.5236585 × 1013
Variance4.9645178 × 1015
MonotonicityNot monotonic
2024-03-19T10:39:30.842898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57192360 26
 
< 0.1%
89196552 26
 
< 0.1%
165048912 26
 
< 0.1%
169490248 23
 
< 0.1%
57180136 15
 
< 0.1%
12194976 15
 
< 0.1%
89162648 15
 
< 0.1%
165040624 15
 
< 0.1%
169467344 15
 
< 0.1%
165045144 13
 
< 0.1%
Other values (123867) 124305
99.8%
ValueCountFrequency (%)
0 11
< 0.1%
2048 1
 
< 0.1%
2056 2
 
< 0.1%
2168 1
 
< 0.1%
3784 1
 
< 0.1%
4224 1
 
< 0.1%
4480 1
 
< 0.1%
4560 1
 
< 0.1%
8280 1
 
< 0.1%
8616 1
 
< 0.1%
ValueCountFrequency (%)
244140480 1
< 0.1%
244138600 1
< 0.1%
244136552 1
< 0.1%
244135688 1
< 0.1%
244133240 1
< 0.1%
244132936 1
< 0.1%
244132752 1
< 0.1%
244131712 1
< 0.1%
244129416 1
< 0.1%
244127840 1
< 0.1%

metric2
Real number (ℝ)

SKEWED  ZEROS 

Distinct558
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.48476
Minimum0
Maximum64968
Zeros118110
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:31.040025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum64968
Range64968
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2179.6577
Coefficient of variation (CV)13.666871
Kurtosis626.82057
Mean159.48476
Median Absolute Deviation (MAD)0
Skewness23.857923
Sum19854896
Variance4750907.8
MonotonicityNot monotonic
2024-03-19T10:39:31.241682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118110
94.9%
2344 281
 
0.2%
8 260
 
0.2%
24 254
 
0.2%
40 201
 
0.2%
4960 175
 
0.1%
424 169
 
0.1%
16 166
 
0.1%
88 152
 
0.1%
552 140
 
0.1%
Other values (548) 4586
 
3.7%
ValueCountFrequency (%)
0 118110
94.9%
8 260
 
0.2%
16 166
 
0.1%
24 254
 
0.2%
32 132
 
0.1%
40 201
 
0.2%
48 90
 
0.1%
56 104
 
0.1%
64 26
 
< 0.1%
72 35
 
< 0.1%
ValueCountFrequency (%)
64968 1
 
< 0.1%
64792 7
< 0.1%
64784 11
< 0.1%
64776 8
< 0.1%
64736 13
< 0.1%
64728 13
< 0.1%
64584 17
< 0.1%
64472 1
 
< 0.1%
64464 1
 
< 0.1%
62296 1
 
< 0.1%

metric3
Real number (ℝ)

SKEWED  ZEROS 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.940455
Minimum0
Maximum24929
Zeros115359
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:31.422211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24929
Range24929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.74732
Coefficient of variation (CV)18.685998
Kurtosis10473.588
Mean9.940455
Median Absolute Deviation (MAD)0
Skewness82.712278
Sum1237527
Variance34502.067
MonotonicityNot monotonic
2024-03-19T10:39:31.564135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 115359
92.7%
1 3274
 
2.6%
2 749
 
0.6%
7 298
 
0.2%
34 293
 
0.2%
5 278
 
0.2%
21 269
 
0.2%
4 268
 
0.2%
9 262
 
0.2%
8 251
 
0.2%
Other values (37) 3193
 
2.6%
ValueCountFrequency (%)
0 115359
92.7%
1 3274
 
2.6%
2 749
 
0.6%
3 113
 
0.1%
4 268
 
0.2%
5 278
 
0.2%
7 298
 
0.2%
8 251
 
0.2%
9 262
 
0.2%
10 241
 
0.2%
ValueCountFrequency (%)
24929 4
 
< 0.1%
2693 179
0.1%
2112 6
 
< 0.1%
1331 240
0.2%
1326 5
 
< 0.1%
1162 1
 
< 0.1%
406 84
 
0.1%
382 5
 
< 0.1%
377 6
 
< 0.1%
323 6
 
< 0.1%

metric4
Real number (ℝ)

SKEWED  ZEROS 

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7411201
Minimum0
Maximum1666
Zeros115156
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:31.738797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum1666
Range1666
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.908507
Coefficient of variation (CV)13.157339
Kurtosis2467.9628
Mean1.7411201
Median Absolute Deviation (MAD)0
Skewness41.502611
Sum216759
Variance524.79967
MonotonicityNot monotonic
2024-03-19T10:39:31.935147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115156
92.5%
6 3681
 
3.0%
1 889
 
0.7%
2 711
 
0.6%
3 466
 
0.4%
12 454
 
0.4%
4 359
 
0.3%
10 294
 
0.2%
112 245
 
0.2%
5 231
 
0.2%
Other values (105) 2008
 
1.6%
ValueCountFrequency (%)
0 115156
92.5%
1 889
 
0.7%
2 711
 
0.6%
3 466
 
0.4%
4 359
 
0.3%
5 231
 
0.2%
6 3681
 
3.0%
7 175
 
0.1%
8 170
 
0.1%
9 45
 
< 0.1%
ValueCountFrequency (%)
1666 9
< 0.1%
1074 6
 
< 0.1%
1033 3
 
< 0.1%
841 1
 
< 0.1%
763 1
 
< 0.1%
533 1
 
< 0.1%
529 4
 
< 0.1%
521 6
 
< 0.1%
487 18
< 0.1%
486 15
< 0.1%

metric5
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.222669
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:32.179159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.943028
Coefficient of variation (CV)1.1209589
Kurtosis12.152135
Mean14.222669
Median Absolute Deviation (MAD)2
Skewness3.4836794
Sum1770637
Variance254.18014
MonotonicityNot monotonic
2024-03-19T10:39:32.404808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 22145
17.8%
9 13597
10.9%
11 12792
10.3%
10 11480
9.2%
7 11271
9.1%
12 9843
7.9%
6 8542
 
6.9%
13 6006
 
4.8%
14 3517
 
2.8%
5 3429
 
2.8%
Other values (50) 21872
17.6%
ValueCountFrequency (%)
1 173
 
0.1%
2 203
 
0.2%
3 815
 
0.7%
4 933
 
0.7%
5 3429
 
2.8%
6 8542
 
6.9%
7 11271
9.1%
8 22145
17.8%
9 13597
10.9%
10 11480
9.2%
ValueCountFrequency (%)
98 224
 
0.2%
95 672
0.5%
94 224
 
0.2%
92 448
0.4%
91 215
 
0.2%
90 357
0.3%
89 224
 
0.2%
78 224
 
0.2%
70 224
 
0.2%
68 448
0.4%

metric6
Real number (ℝ)

Distinct44838
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260172.66
Minimum8
Maximum689161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:32.724038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221452
median249799.5
Q3310266
95-th percentile443047.8
Maximum689161
Range689153
Interquartile range (IQR)88814

Descriptive statistics

Standard deviation99151.079
Coefficient of variation (CV)0.38109723
Kurtosis1.9077772
Mean260172.66
Median Absolute Deviation (MAD)35382.5
Skewness-0.37528461
Sum3.2389935 × 1010
Variance9.8309364 × 109
MonotonicityNot monotonic
2024-03-19T10:39:33.023311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 777
 
0.6%
44 708
 
0.6%
27 636
 
0.5%
26 520
 
0.4%
29 441
 
0.4%
36 337
 
0.3%
35 290
 
0.2%
52 282
 
0.2%
45 246
 
0.2%
28 216
 
0.2%
Other values (44828) 120041
96.4%
ValueCountFrequency (%)
8 19
 
< 0.1%
9 172
0.1%
12 51
 
< 0.1%
18 36
 
< 0.1%
19 30
 
< 0.1%
20 6
 
< 0.1%
21 58
 
< 0.1%
23 71
 
0.1%
24 123
0.1%
25 184
0.1%
ValueCountFrequency (%)
689161 1
< 0.1%
689062 1
< 0.1%
689035 1
< 0.1%
688964 1
< 0.1%
688952 2
< 0.1%
687901 1
< 0.1%
687802 1
< 0.1%
687775 1
< 0.1%
687706 1
< 0.1%
687694 2
< 0.1%

metric7
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29252815
Minimum0
Maximum832
Zeros123036
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:33.282192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.436924
Coefficient of variation (CV)25.422934
Kurtosis6876.273
Mean0.29252815
Median Absolute Deviation (MAD)0
Skewness73.476456
Sum36418
Variance55.307838
MonotonicityNot monotonic
2024-03-19T10:39:33.442766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123036
98.8%
8 793
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 35
 
< 0.1%
128 23
 
< 0.1%
40 20
 
< 0.1%
176 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 56
 
< 0.1%
ValueCountFrequency (%)
0 123036
98.8%
6 13
 
< 0.1%
8 793
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 35
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric8
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29252815
Minimum0
Maximum832
Zeros123036
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:33.606468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.436924
Coefficient of variation (CV)25.422934
Kurtosis6876.273
Mean0.29252815
Median Absolute Deviation (MAD)0
Skewness73.476456
Sum36418
Variance55.307838
MonotonicityNot monotonic
2024-03-19T10:39:33.812271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123036
98.8%
8 793
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 35
 
< 0.1%
128 23
 
< 0.1%
40 20
 
< 0.1%
176 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 56
 
< 0.1%
ValueCountFrequency (%)
0 123036
98.8%
6 13
 
< 0.1%
8 793
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 35
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric9
Real number (ℝ)

SKEWED  ZEROS 

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.451524
Minimum0
Maximum18701
Zeros97358
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2024-03-19T10:39:34.003857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum18701
Range18701
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.42562
Coefficient of variation (CV)15.37367
Kurtosis4050.1905
Mean12.451524
Median Absolute Deviation (MAD)0
Skewness49.899278
Sum1550140
Variance36643.769
MonotonicityNot monotonic
2024-03-19T10:39:34.147702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97358
78.2%
1 9436
 
7.6%
2 3722
 
3.0%
3 2327
 
1.9%
4 1396
 
1.1%
6 797
 
0.6%
7 774
 
0.6%
5 735
 
0.6%
8 733
 
0.6%
10 641
 
0.5%
Other values (55) 6575
 
5.3%
ValueCountFrequency (%)
0 97358
78.2%
1 9436
 
7.6%
2 3722
 
3.0%
3 2327
 
1.9%
4 1396
 
1.1%
5 735
 
0.6%
6 797
 
0.6%
7 774
 
0.6%
8 733
 
0.6%
9 335
 
0.3%
ValueCountFrequency (%)
18701 5
 
< 0.1%
10137 4
 
< 0.1%
7226 5
 
< 0.1%
2794 6
 
< 0.1%
2637 84
0.1%
2522 179
0.1%
2270 5
 
< 0.1%
2269 1
 
< 0.1%
1864 5
 
< 0.1%
1165 118
0.1%

Interactions

2024-03-19T10:39:25.687242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:11.499854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:14.098361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:15.987471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.571386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:19.107713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:20.801909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.523920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:24.143164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:25.866491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:11.647314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:14.300915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:16.169918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.758410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:19.315162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:21.008857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.706282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:24.307302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.047616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:11.845420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:14.530592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:16.347677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.947054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:19.502795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:21.406486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.857073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:24.525419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.221738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:12.066280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:14.714457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:16.497768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.140602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:19.660798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:21.574470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.999903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:24.698369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.384560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:12.432223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:14.873295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:16.705163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.286707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:19.852094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:21.759507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:23.186231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:24.916164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.529932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:12.722109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:15.044421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:16.864057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.440701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:20.003339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:21.944451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:23.337488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:25.049865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.694283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:12.951514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:15.278791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.055691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.623826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:20.189150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.102978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:23.533023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:25.237540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:26.871497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:13.091954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:15.530679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.238936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.776802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:20.351538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.262574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:23.733235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:25.348835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:27.034748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:13.912369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:15.739571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:17.388134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:18.919183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:20.584560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:22.384009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:23.898959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-19T10:39:25.519686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-19T10:39:34.304642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
failuremetric1metric2metric3metric4metric5metric6metric7metric8metric9
failure1.0000.0020.0540.0030.0580.0040.0030.0970.0970.005
metric10.0021.000-0.0010.0020.002-0.005-0.003-0.002-0.002-0.003
metric20.054-0.0011.000-0.0190.225-0.027-0.0770.1090.109-0.029
metric30.0030.002-0.0191.0000.1210.1070.070-0.010-0.0100.390
metric40.0580.0020.2250.1211.000-0.0210.0120.1630.1630.049
metric50.004-0.005-0.0270.107-0.0211.0000.084-0.020-0.0200.034
metric60.003-0.003-0.0770.0700.0120.0841.000-0.016-0.0160.090
metric70.097-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.018
metric80.097-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.018
metric90.005-0.003-0.0290.3900.0490.0340.090-0.018-0.0181.000

Missing values

2024-03-19T10:39:27.310265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-19T10:39:27.645082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datedevicefailuremetric1metric2metric3metric4metric5metric6metric7metric8metric9
01/1/15S1F010850215630672560526407438007
11/1/15S1F0166B0613706800306403174000
21/1/15S1F01E6Y017329596800012237394000
31/1/15S1F01JE00796940240006410186000
41/1/15S1F01R2B013597048000015313173003
51/1/15S1F01TD506883748800416413535001
61/1/15S1F01XDJ02277216320008402525000
71/1/15S1F023H201415036000011949446216163
81/1/15S1F02A0J0821784001014311869000
91/1/15S1F02DZ2011644009603239940790500164
datedevicefailuremetric1metric2metric3metric4metric5metric6metric7metric8metric9
12448411/2/15W1F0SJJ204752532000012357421000
12448511/2/15Z1F0GB8A0928231920009357127000
12448611/2/15Z1F0GE1M022287870400010349826000
12448711/2/15Z1F0KJDS07988364800011358121000
12448811/2/15Z1F0KKN402187657120009353525000
12448911/2/15Z1F0MA1S01831022400010353705880
12449011/2/15Z1F0Q8RT0172556680961074113327920013
12449111/2/15Z1F0QK0501902912048320011350410000
12449211/2/15Z1F0QL3N022695340800012358980000
12449311/2/15Z1F0QLC101757284000010351431000

Duplicate rows

Most frequently occurring

datedevicefailuremetric1metric2metric3metric4metric5metric6metric7metric8metric9# duplicates
07/10/15S1F0R4Q8019272139200082137000002